The detection of military and intelligence targets by airborne autonomous electro-optical reconnaissance systems is an important evolving capability associated with the maturation of hyperspectral (HS) camera technology. In recent years it has become clear that the primary factor limiting the effectiveness of these systems is no longer hardware performance, but rather the absence of useful knowledge about the in situ spectra of the targets. A similar problem exists in commercial applications. Laboratory reflectance spectra of materials sought by remote sensing systems must be translated into the spectral intensities measured in the field with an imaging spectrometer.
The HS detection algorithms that have been exercised by numerous researchers over the past decade basically fall into two categories. The first category, called anomaly detectors, relies on the simple fact that most manmade objects have spectral signatures different from the background against which they appear. Typically, these signatures are sufficiently distinct that automatic detection can be achieved with no prior knowledge of the spectrum of a particular target. However, all such algorithms to date have met a performance barrier. False alarm rates can be lowered no further, unless some type of supplementary information is provided, or the detection goals are narrowed (e.g., to the detection of changes only). Most importantly, operational stand-alone systems require false alarm detection rates one to two orders of magnitude lower than currently achievable with anomaly detectors.
The second category of detection algorithms relies on prior information, of variable precision, about the spectral signature of the target of interest. Such algorithms are intrinsically more capable than anomaly detectors because they exploit some knowledge of the target. These methods enhance performance in reconnaissance missions, and are essential for remote prospecting, in which mineral maps are generated from HS imagery.
The simplest signature-based method, called template matching, is appropriate when the desired sensed target spectrum, consisting of the spectral radiant intensities measured by a remote sensor, can be predicted accurately. Detection is achieved by simply comparing the prediction to the spectrum derived from each test pixel produced by a calibrated HS device. Such methods are most appropriate for detecting simple compounds under certain conditions (for example, exposed chemicals).
However, many intelligence applications require the detection of targets with imperfectly known signatures, making template matching unreliable. In commercial remote sensing applications, it is difficult to translate laboratory (reflectance or emittance) spectra into sensed (radiant energy) spectra, depending on the ambient illumination and a myriad of environmental conditions. To summarize, for remotely deployed systems, ideal knowledge of target signature information is seldom available.
The target variability can be broken down into two classes: static and dynamic. For manmade targets, sources of static variability include intrinsic features associated with paint constituents, alterations caused by weathering (oxidation) and the contamination of surface signatures by dirt. In addition, mixed pixels containing both target and background elements exhibit apparent target variability that derives ultimately from background variability. For geological mapping, unpredictable mixtures of materials and intrinsic spectral variability in complex minerals complicate the problem. Imperfect sensing generates more uncertainty in all cases. These combined effects lead to inexact target signature representations and are practically impossible to compensate for in a remote detection operation. If, however, the uncertainty can be characterized statistically, then optimal detectors, i.e., detector algorithms, can still be devised.
However, the primary limitation to detection performance arises from apparent (i.e., sensed) target signature variations that occur over time. This dynamic variability is substantial in remote sensing applications over periods as short as a few hours, and many intelligence applications require revisits within 24 hours. This dynamic variability also degrades the performance of a powerful terrain mapping method, in which a mineral or material is identified at one site, its spectrum is collected remotely, and this signature must be translated for use at another site, or at another time. The imprecision in target signature knowledge limits how low even an optimal false alarm or misclassification rate for the algorithm can be driven.
Sources of the dynamic signature variability, include such things as differing levels of background illumination associated with variable sun angles, unpredictable changes in sky illumination caused by clouds, and reflections from changed local backgrounds for targets that have moved. Diurnal changes in the atmosphere, especially those associated with haze, as well as in the moisture content of vegetation, also contribute to differences in the apparent spectra of backgrounds and targets. Finally, all sensing systems have some time-varying inconsistencies in their responses to the physical environment.
Considering some of the standard methods used in autonomous target detection with an HS system, a typical sensor is a digital camera modified to collect several, often many, contiguous wavelengths. The system requires data storage and computing devices and may include a communication downlink to a ground station. Instead of the three colors (wavelengths) typically collected by a commercial digital camera, an HS sensor used for reconnaissance often collects many hundreds of wavelengths. The associated large volume of information dictates that only a limited amount of streaming data can be stored onboard or telemetered in real time to the ground.
Instead of attempting to maintain an unwieldy database of all collected data, autonomous detection systems store statistical summaries describing the sensed background. These include the conventional measures of mean radiance μi, and variance σi2 for each sensed color i. If N wavelengths are collected, the mean values are arranged in the form of a vector μi, an array (column) of N numbers, each of which represents the mean measured radiance in a different wavelength over some surveilled area. The variance is replaced by a two-dimensional array of numbers called a covariance matrix M. Its diagonal entries Mii are the conventional variances σi2 for each color. However, the off-diagonal of matrix M entries encode additional information in the form of correlations between color channels, i.e., mutual dependencies in the measured radiances that characterize the particular background being reconnoitered.
Most experimental HS detection systems use these data to construct some form of anomaly detection algorithm. The most common form of anomaly detector is based on the so-called RX algorithm (see Reed, I. S. and X. Yu, Adaptive multi-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE, Trans. Acoustics, Speech, and Signal Processing, 38(10), (1990), which is hereinafter referred to as the Reed et al reference and which is hereby incorporated by reference). The basic RX detector consists of comparing the computed test statistics=(x−μ)tM−1(x−μ)  (1)
to a number, called the threshold. The vector x is measured radiance at any test pixel, M−1 is the inverse of the covariance matrix, and t refers to matrix transposition. (Computation of M and μ are described in Appendix 1 below.) The matrix multiplication in Equation (1) converts these quantities into a different number s for every pixel x that is encountered. If s exceeds the threshold, then the decision target is made by an onboard signal processor. Otherwise, the pixel is labeled background. A false alarm occurs when a background pixel generates a threshold exceedance, causing it to acquire a false label. The threshold is defined adaptively, based on the most recent estimates of first-and-second-order statistics, as represented by the mean values and covariance matrices. Combining this information with some standard theoretical assumptions produces a threshold value designed to keep the rate of false detections at a controlled low level.
This pixel-level detection is often followed by some simple form of spatial processing. For example, if the number of contiguous pixels expected to cover a given target shape is known, then a similar grouping of detections may also be used as a criterion for target declaration. It is noted that this type of spatial post-processing is unaffected by the methods of the present invention.
The lack of dependable target signature information has always made reliance on some form of anomaly detection necessary in autonomous HS detection systems. However, as indicated above, all such methods suffer limited performance in the form of excessive false alarm rates. The same problem limits the accuracy of mineral mapping techniques based on remote sensing.